PolarToIm

线性同余伪随机数发生器、小数开方伪随机数发生器、利用Logistic方程和混合光学双稳模型产生混沌序列产生的序列矩阵(Linear congruence pseudo-random number generator, decimal square pseudo-random number generator, using Logistic equation and hybrid optical bistable model to generate sequence matrix generated by chaotic sequence)

在图像处理过程中盲解卷积也是一个很好地图像去噪的方法。(In the image processing method of blind deconvolution is also a good image denoising.)

说明：  滤波是将信号中特定波段频率滤除的操作，是抑制和防止干扰的一项重要措施，滤波分为经典滤波和现代滤波。(Filtering is the operation of filtering out the specific band frequency of the signal, which is an important measure to suppress and prevent interference. Filtering is divided into classical filtering and modern filtering.)

计算图像的质心，各种矩以及基于矩的图像重构算法，对模式识别和图像处理有较大的研究价值。(calculated image of the center of mass, and for the moment the various moments of image reconstruction algorithm, on Pattern Recognition and Image Processing of a larger research value.)

直方图均衡化，包括动态直方图均衡化，全局直方图均衡化，局部直方图均衡化，以及绘制图像的直方图 其中动态直方图均衡化用A dynamic histogram equalization for image contrast enhancement 只取x=0情况(Histogram equalization, including dynamic histogram equalization, global histogram equalization, local histogram equalization, and where to draw the image histogram dynamic histogram equalization with A dynamic histogram equalization for image contrast enhancement only take x = 0 Happening)

计算局部形状描述符梯度方向直方图（HOG），直接输入图像即可用。(Calculate the local shape descriptors gradient orientation histogram (HOG), the input image can be used directly.)

说明：  实现DCT与DWT相结合的算法,用matable及小波变换实现所想构思。(The algorithm of combining DCT with DWT is realized, and the idea is realized by matable and wavelet transform.)

a file abaut image segmentation

基于马尔可夫随机场的图像恢复，给出产生MRF的程序并应用于图像去噪(GMRF based on the image restoration, MRF is generated and applied to the procedures Image Denoising)

斯坦福大学的姚邦鹏开发的一个图像分类算法，使用random forest实现了图像的精细区域描述，赢得了PASCAL VOC2011图像分类竞赛中的winner prize.经测试，程序完全可运行，且提供了MAC和Windows下的两种程序。(Image Classification: An Integration of Randomization and Discrimination in A Dense Feature Representation The goal of our method is to identify the discriminative fine-grained image region that distinguishes different classes. To achieve this goal we sample image regions from dense sampling space and use a random forest algorithm with discriminative classifier. Each node of the tree of random forest is trained and tested with fine-grained image patches combining the information from upstream nodes together. We implemented each node of the tree with a discriminative SVM classifier, which makes the node as a strong classifier. )